Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-447-2019
https://doi.org/10.5194/hess-23-447-2019
Research article
 | 
25 Jan 2019
Research article |  | 25 Jan 2019

Statistical approaches for identification of low-flow drivers: temporal aspects

Anne Fangmann and Uwe Haberlandt

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Cited articles

Akaike, H.: New Look at Statistical-Model Identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/Tac.1974.1100705, 1974. 
Coles, S.: An introduction to statistical modeling of extreme values, Springer, London, 2001. 
Dai, A. G.: Increasing drought under global warming in observations and models, Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/Nclimate1633, 2013. 
de Wit, M. J. M., van den Hurk, B., Warmerdam, P. M. M., Torfs, P. J. J. F., Roulin, E., and van Deursen, W. P. A.: Impact of climate change on low-flows in the river Meuse, Climatic Change, 82, 351–372, https://doi.org/10.1007/s10584-006-9195-2, 2007. 
Fangmann, A.: Low flow prediction in time and space: An adaptive statistical scheme for regional climate change impact assessment, Mitteilungen, Heft 106, Inst. of Hydrology, Leibniz University of Hannover, Hannover, 160 pp., 2017. 
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Short summary
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can be found, based on simple statistical relationships between low-flow metrics and meteorological states, that can help identify potential low-flow drivers. In this study we assess whether such relationships exist and whether they can be applied to predict future low flow within regional climate change impact assessment in the northwestern part of Germany.
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